Spectrum and Prognosis of Antineutrophil Cytoplasmic Antibody–associated Vasculitis-related Bronchiectasis: Data from 61 Patients
Bibliographic record
Abstract
Objective To report on a large series of patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) and bronchiectasis, with a specific focus on the timeline of occurrence of both features. Methods Retrospective nationwide multicenter study of patients diagnosed with both AAV and bronchiectasis. Results Sixty-one patients were included, among whom 27 (44.25%) had microscopic polyangiitis (MPA), 27 (44.25%) had granulomatosis with polyangiitis (GPA), and 7 (11.5%) had eosinophilic GPA. Thirty-nine (64%) had myeloperoxidase (MPO)-ANCA and 13 (21%) had proteinase 3–ANCA. The diagnosis of bronchiectasis either preceded (n = 25; median time between both diagnoses: 16 yrs, IQR 4–54 yrs), was concomitant to (n = 12), or followed (n = 24; median time between both diagnoses: 1, IQR 0–6 yrs) that of AAV. Patients in whom bronchiectasis precedes the onset of AAV (B-AAV group) have more frequent mononeuritis multiplex, MPA, MPO-ANCA, and a 5-fold increase of death. The occurrence of an AAV relapse tended to be protective against bronchiectasis worsening (HR 0.6, 95% CI 0.4–0.99, P = 0.049), while a diagnosis of bronchiectasis before AAV (HR 5.8, 95% CI 1.2–28.7, P = 0.03) or MPA (HR 18.1, 95% CI 2.2–146.3, P = 0.01) were associated with shorter survival during AAV follow-up. Conclusion The association of bronchiectasis with AAV is likely not accidental and is mostly associated with MPO-ANCA. Patients in whom bronchiectasis precedes the onset of AAV tend to have distinct clinical and biological features and could carry a worse prognosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".